LangGraph vs Mastra
Detailed side-by-side comparison to help you choose the right tool
LangGraph
🔴DeveloperAI agent framework
LangGraph is LangChain’s framework for reliable agents with low-level control, deployment, observability, evaluation, sandboxes and enterprise LangSmith services.
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FreeMastra
🔴DeveloperAI agent framework
Mastra is a TypeScript-first AI agent framework and platform for building production agents with workflows, memory, MCP, evals, observability, and deployment.
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FreeFeature Comparison
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💡 Our Take
Choose Mastra if your team wants a TypeScript-first agent framework with hosted deployment and observability. Choose LangGraph if you prefer the LangChain ecosystem and need graph-based state machines with broad Python adoption.
LangGraph - Pros & Cons
Pros
- ✓Excellent when you need deterministic agent control instead of one-shot prompt chains.
- ✓Pairs naturally with LangSmith for traces, evals, deployments, and production debugging.
- ✓The graph model makes approval steps, retries, routing, and long-running workflows easier to reason about.
Cons
- ✗More engineering-heavy than no-code builders; teams need Python/TypeScript skill and agent architecture discipline.
- ✗Pricing is split across framework and LangSmith services, so total cost depends on usage and deployment choices.
- ✗Overkill for simple chatbots or single API-call automations.
Mastra - Pros & Cons
Pros
- ✓Strong TypeScript fit for product teams already building in Next.js, Express, Hono, or similar JavaScript stacks
- ✓Combines framework, memory, workflows, evals, observability, and deployment instead of forcing teams to assemble every production feature separately
- ✓Apache 2.0 open-source framework gives teams a free self-hosted starting point before adopting the hosted platform
- ✓Public pricing includes useful operational limits such as observability events, CPU hours, retention, egress, and memory token usage
- ✓MCP support makes Mastra easier to connect with the growing ecosystem of agent tools and external capabilities
Cons
- ✗Developer-first framework; non-technical teams looking for a visual bot builder will likely move faster with Dify or a no-code platform
- ✗Usage-based overages for observability events, CPU time, egress, retrieval storage, and memory tokens require monitoring in production
- ✗Python-heavy teams may prefer OpenAI Agents SDK, Pydantic AI, or LangGraph rather than adding TypeScript to the agent stack
- ✗Production success still depends on careful eval design, tool permissions, security review, and rollback planning
- ✗Enterprise-grade controls such as RBAC, audit logs, dedicated SLAs, and VPC-style deployment are custom-priced rather than included in Starter
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